Update 414-startup-row.txt

Author: manikanta-hitunik-com

transcripts/414-startup-row.txt

@@ -2,7 +2,7 @@
 
 00:00:27 [Music]
 
-00:00:40 Welcome to Talk Pythonomy, a weekly podcast on Python. This is your host, Michael Kennedy.
+00:00:40 Welcome to Talk Python to Me, a weekly podcast on Python. This is your host, Michael Kennedy.
 
 00:00:45 Follow me on Mastodon, where I'm @mkennedy, and follow the podcast using @talkpython, both on fosstodon.org. Be careful with impersonating accounts on other instances, there are many. Keep up with the show and listen to over seven years of past episodes at talkpython.fm.
 
@@ -12,7 +12,7 @@
 
 00:01:53 And I just thought it'd be so great to have you on the show along with all the others and just kind of tell your story.
 
-00:01:58 You know, how did you, how did you get here to start up Rho at PyCon?
+00:01:58 You know, how did you, how did you get here to start up Row at PyCon?
 
 00:02:01 - Yeah, it's interesting.
 
@@ -72,7 +72,7 @@
 
 00:04:24 Open source modem also integrates with Dask clusters as well.
 
-00:04:27 So Dask has Dask DataFrame and that runs on Dask clusters.
+00:04:27 So Dask has Dask Data Frame and that runs on Dask clusters.
 
 00:04:31 We can also run a modem open source on Dask clusters.
 
@@ -242,7 +242,7 @@
 
 00:09:13 And what's the problem you're solving here?
 
-00:09:15 - Yeah, we, kind of as it says on the tin, we're working on artificial general intelligence.
+00:09:15 - Yeah, we, kind of as it says on the tim, we're working on artificial general intelligence.
 
 00:09:20 We don't usually like to use that term 'cause it can mean lots of different things to lots of different people, but in general, what we're working on is making more capable, safer, more robust AI systems.
 
@@ -482,7 +482,7 @@
 
 00:17:34 [AUDIO OUT]
 
-00:17:36 Now we talk with Mo Sarat from Werabots.
+00:17:36 Now we talk with Mo Sarat from Wherobots.
 
 00:17:39 They're building the database platform for geospatial analytics and AI.
 
@@ -500,9 +500,9 @@
 
 00:17:49 Absolutely.
 
-00:17:50 So my name is Mo, and I'm the co-founder and CEO of a company called Werabots.
+00:17:50 So my name is Mo, and I'm the co-founder and CEO of a company called Wherobots.
 
-00:17:55 Werabots' grand vision is enable every organization to drive value from data via space and time.
+00:17:55 Wherobots' grand vision is enable every organization to drive value from data via space and time.
 
 00:18:00 Awesome.
 
@@ -512,7 +512,7 @@
 
 00:18:02 So yeah, thanks for being here on the show.
 
-00:18:04 Let's dive into whereabouts of what is the problem you're solving?
+00:18:04 Let's dive into wherobots of what is the problem you're solving?
 
 00:18:08 What are you guys building?
 
@@ -610,7 +610,7 @@
 
 00:21:40 So these are just a couple of use cases, but there are tons of other use cases and use cases that may not exist even yet.
 
-00:21:47 So there's a lot of movement now into climate tech and AgTech, and we are, like, what we're trying to do at WeraBots is we're building the database infrastructure that enable the next generation climate tech and agriculture technology.
+00:21:47 So there's a lot of movement now into climate tech and AgTech, and we are, like, what we're trying to do at Wherobots is we're building the database infrastructure that enable the next generation climate tech and agriculture technology.
 
 00:22:01 - So they can ask the questions that they might have, but you already have the machinery to answer them.
 
@@ -654,7 +654,7 @@
 
 00:23:17 It doesn't scale, all that kind of stuff.
 
-00:23:19 So what we do at WorldBots is that we provide SQL API to the user to run spatial queries on the data, but we also provide a spatial Python API.
+00:23:19 So what we do at Wherobots is that we provide SQL API to the user to run spatial queries on the data, but we also provide a spatial Python API.
 
 00:23:29 Like if you're using Geopandas, you can use the same API, do the heavy lifting enterprise scale, kind of processing of the data using our platform, and then do the major Geopandas kind of functionality you're familiar with to, again, do the geospatial processing with it.
 
@@ -676,13 +676,13 @@
 
 00:24:16 - Absolutely, yeah.
 
-00:24:17 - So it sounds like your business, Wearbots, is a little bit following the open core model, you say?
+00:24:17 - So it sounds like your business, Wherobots, is a little bit following the open core model, you say?
 
 00:24:23 - Yes.
 
 00:24:24 - Let's round out our conversation here with talking about the business itself.
 
-00:24:27 How'd you get to startup, Ro?
+00:24:27 How'd you get to startup row?
 
 00:24:29 - We follow the open core model.
 
@@ -730,7 +730,7 @@
 
 00:25:33 - Yep, bye.
 
-00:25:33 - Time to talk to Neptime, who have created Python programmable spreadsheets that are super powered with Python and AI.
+00:25:33 - Time to talk to Neptyne, who have created Python programmable spreadsheets that are super powered with Python and AI.
 
 00:25:40 I gotta tell you, this product looks super awesome.
 
@@ -758,9 +758,9 @@
 
 00:26:02 I'm Dawa's co-founder.
 
-00:26:04 Been doing Python a little less than that, but met Dow about five years ago, and we founded Neptine about a year ago.
+00:26:04 Been doing Python a little less than that, but met Dow about five years ago, and we founded Neptyne about a year ago.
 
-00:26:10 - Yeah, so let's dive into Neptine.
+00:26:10 - Yeah, so let's dive into Neptyne.
 
 00:26:14 What's the product, what's the problem you're solving?
 
@@ -840,7 +840,7 @@
 
 00:29:14 - Yeah, how interesting.
 
-00:29:16 There's a little window where you can write straight Python, you know, some function that does arbitrary Python, and then you can invoke it like a function in the spreadsheet, right?
+00:29:16 There's a little window where you can write straight Python, you know, dev some function that does arbitrary Python, and then you can invoke it like a function in the spreadsheet, right?
 
 00:29:25 - Exactly, exactly.
 
@@ -866,7 +866,7 @@
 
 00:29:51 And that's kind of, we do that for maximum flexibility, maximum capability.
 
-00:29:55 So it means that anything you can install, anything you can run on a Jupyter notebook running on Linux, you can run in Neptune.
+00:29:55 So it means that anything you can install, anything you can run on a Jupyter notebook running on Linux, you can run in Neptyne.
 
 00:30:02 - I see, so we get full Python 3.11 or 3.10 or whatever it is.
 
@@ -884,7 +884,7 @@
 
 00:30:28 You can try it out.
 
-00:30:29 You can go to neptine.com in the upper right.
+00:30:29 You can go to neptyne.com in the upper right.
 
 00:30:31 Just click log in.
 
@@ -898,7 +898,7 @@
 
 00:30:38 - All right, final question.
 
-00:30:40 How'd you guys get here to start up Rho?
+00:30:40 How'd you guys get here to start up Row?
 
 00:30:42 Everyone wants to build something amazing with open source, but how did you turn that into a business and something you can put your full time into?
 
@@ -932,7 +932,7 @@
 
 00:31:51 - Thank you so much.
 
-00:31:52 - Now up is Nixle.
+00:31:52 - Now up is Nixtla.
 
 00:31:53 We have Federico Garza and Christian Chula here to tell us about their time series startup, ready to make predictions based on an open source time series ecosystem.
 
@@ -946,11 +946,11 @@
 
 00:32:07 Who are y'all?
 
-00:32:08 - So I am Christian Chalhoun, I'm a co-founder of Nixla.
+00:32:08 - So I am Christian Chalhoun, I'm a co-founder of Nixtla.
 
 00:32:11 - Yep.
 
-00:32:12 - Hello, I'm Fede, I'm CTO and co-founder of Nixla.
+00:32:12 - Hello, I'm Fede, I'm CTO and co-founder of Nixtla.
 
 00:32:15 - Nice to meet you both.
 
@@ -960,7 +960,7 @@
 
 00:32:20 And yeah, let's start with the problem y'all are trying to solve.
 
-00:32:24 OK, yeah, so at NixLab, what we do is time series forecasting.
+00:32:24 OK, yeah, so at Nixtla, what we do is time series forecasting.
 
 00:32:28 So as you know, time series forecasting is a very relevant task that a lot of companies and practitioners need to solve.
 
@@ -1140,7 +1140,7 @@
 
 00:37:00 - Thanks. - Bye.
 
-00:37:01 - We'll speak with Piero Molina from Predebase.
+00:37:01 - We'll speak with Piero Molina from Predibase.
 
 00:37:03 They empower you to rapidly build, iterate, and deploy ML models with their declarative machine learning platform.
 
@@ -1222,7 +1222,7 @@
 
 00:40:32 That's a good analogy.
 
-00:40:34 And so, Pride Debase, what does it do?
+00:40:34 And so, PriDebase, what does it do?
 
 00:40:35 It uses this basic concept of models as configuration, really, and builds on top of it all sorts of infrastructure that is needed for organizations that are big enterprises to use it in the cloud.
 
@@ -1312,7 +1312,7 @@
 
 00:44:04 - Yeah, bye. - Thank you so much.
 
-00:44:05 - We'll finish up our stroll down startup lane by talking with the folks at Pinecone.
+00:44:05 - We'll finish up our stroll down startup lane by talking with the folks at Pynecone.
 
 00:44:08 We have Nikhil Rao to talk about the PurePython Fullstack web app platform that they've built.
 
@@ -1330,7 +1330,7 @@
 
 00:44:26 Yeah, yeah, give a quick introduction on yourself.
 
-00:44:29 - Yeah, so I'm Nikhil, I'm the CEO co-founder of Pinecone and we're building a way to make web apps in pure Python.
+00:44:29 - Yeah, so I'm Nikhil, I'm the CEO co-founder of Pynecone and we're building a way to make web apps in pure Python.
 
 00:44:35 So we have an open source framework and anyone can install this and basically start creating their apps front end and back end using Python.
 
@@ -1426,7 +1426,7 @@
 
 00:47:33 - Yes, exactly.
 
-00:47:34 So I've used tools like Streamlit, Gradio in the past, and a lot of that was inspiration for Pinecone.
+00:47:34 So I've used tools like Streamlit, Gradio in the past, and a lot of that was inspiration for Pynecone.
 
 00:47:39 It's really great 'cause it's super easy to get started with, you don't have to go learn these things, but they all have this kind of ceiling you hit.
 
@@ -1462,7 +1462,7 @@
 
 00:48:48 We just leverage React.
 
-00:48:49 And what we do in Pinecone for the front end is we just wrap React components and make them accessible.
+00:48:49 And what we do in Pynecone for the front end is we just wrap React components and make them accessible.
 
 00:48:54 So even if we don't offer something, and other low-code tools, sometimes if they don't offer a component you need, you may be kind of constrained in what you can build.
 
@@ -1522,7 +1522,7 @@
 
 00:50:21 How'd you start the company?
 
-00:50:22 How'd you land on Startup Pro?
+00:50:22 How'd you land on Startup row?
 
 00:50:24 I mean, you talked about Y Combinator a little.
 
@@ -1591,4 +1591,3 @@
 00:52:28 [Music]
 
 00:52:43 (upbeat music)
-